Improved Support Vector Machine Research Articles

A novel automatic detection and classification algorithm for elderly cognitive impairment using CSVM

Alzheimer’s disease (AD) is a degenerative disease with insidious onset and chronic nervous system, which can be roughly classified into three stages: preclinical AD, mild cognitive impairment (MCI), and clinical AD. Given that timely treatment of MCI can delay the course of AD, identifying what stage of the disease is at the time of early diagnosis is critical in clinical practice. Based on the survey data of elderly residents in Changsha community, this paper constructs the cognitive impairment data set and explores the application of the emerging classification algorithm Convolutional Support Vector Machine(CSVM) in identifying elderly cognitive impairment, a small data classification problem. CSVM is an improved Support Vector Machine classification(SVC) algorithm, which uses the convolution filtering idea of the Convolutional Neural Networks(CNN) algorithm to preprocess the nonlinearly separable sample set to improve the classification ability of SVM. In addition, the optimal convolution filter (solution) is found by the Simplified Swarm Optimization(SSO) algorithm and orthogonal array experimental design. A variable update amplitude method is also proposed to optimize the SSO update strategy further. Aiming at the problem of an unbalanced sample set, a stratified sampling method is used to divide and cross-validate the sample data, and a variety of evaluation indicators are used to evaluate the performance of the model. Experimental results show that the data set processed by a specific convolution filter can significantly improve the classification performance of linear SVM. The accurate differentiation of normal cognition(NC), MCI, and AD helps intervene in adjuvant treatment for potential patients timely.

Identification of market power abuse in Chinese electricity market based on an improved cost-sensitive support vector machine

Accurate and real-time identification of market power abuse is a key task in the management of electricity market violations. However, there are few effective monitoring methods for extremely imbalanced datasets and progressively increasing amounts of data in actual market transactions. To address the aforementioned problems, this paper proposes an improved support vector machine by considering the index system, which can not only realize identification automatically but also minimize the credit risk of power market transactions. Firstly, the dataset is composed of an indicator system for measuring market power abuses. Secondly, a comprehensive algorithm for identifying offending data is proposed, which combines the K-Nearest Bound Neighbor and the distance between the means of two classes methods to overcome the shortcomings of traditional support vector machines with long training time due to the high dimensionality and progressively increasing amounts of data in actual market transactions, and the Cost-sensitive Support Vector Machine to tackle the problem of inefficient identification due to few tags in transaction data. Finally, five different features of constructed datasets and a power market synthetic dataset are tested, and results indicate that the proposed method can ensure high classification accuracy while significantly improving recognition speed and recall for violation data, which is more suitable for Chinese electricity market data and provide a dynamic detection method to identify market power abuse precisely and quickly.

A novel dynamic risk assessment method for hazardous chemical warehouses based on improved SVM and mathematical methodologies

Effective dynamic risk assessment is crucial for identifying process hazards and preventing accidents. The rapid development of modern technology urgently requires the development of new dynamic risk assessment methods to address current societal needs, especially in situations where a large amount of real-time data is available. Consequently, this manuscript presents an improved support vector machine model (SVM), integrated mathematical modeling, to facilitate dynamic risk assessment for hazardous chemical warehouses (HCWs). Firstly, an indicator framework and a quantitative risk factor table are crafted. Then a mathematical model using knowledge graph and DEMATEL-variable weight theory (VWT) is established, which combines accident information with VWT to optimize the weight calculation and provides reliable prior knowledge for the improved SVM model. Ultimately, the improved SVM model is crafted by the establishment of an improved hybrid kernel of polynomial and radial basis function kernel, fine-tuning its hyperparameters with particle swarm optimization, and utilizing sensitivity analysis to simplify the model. The model is formulated to accommodate the intricate non-linear relationships within indicators and dynamic risk, thereby establishing dynamic risk assessment rules. Through a case study, the improved SVM model demonstrates higher precision in dynamic risk prediction compared to original models. The results substantiate that the model can provide innovative insight and methodology for the precise evaluation of dynamic risk in HCWs.

A MENTAL ILLNESS EARLY WARNING ANALYSIS MODEL USING AN IMPROVED SVM ALGORITHM AND EEG SIGNALS

The fast-paced lifestyle, intense competition, and constantly changing environment of modern society are important factors contributing to the emergence of psychological disorders. Factors such as work pressure, family conflicts, and interpersonal relationship difficulties pose challenges to individual mental health. Prolonged use of electronic devices and the widespread use of social media further exacerbate mental health issues, as the constant influx of information makes it difficult for people to relax and focus. The harm of psychological disorders to individuals and society cannot be ignored. It not only diminishes the quality of life for patients but also affects work efficiency and social participation. In extreme cases, psychological disorders can lead to serious consequences like suicide, causing immense pain to families and society. Therefore, prioritizing mental health, raising awareness of mental well-being, and timely intervention and treatment of psychological disorders are crucial for individual happiness and social stability. Based on this, this study employs an improved Support Vector Machine (SVM) and Electroencephalogram (EEG) signals for the diagnosis of psychological disorders, aiming for more effective treatment of mental health conditions. Specifically, the EEG signals of the experimental subjects are first collected as training data for the model. The dataset undergoes preprocessing operations such as cleaning and normalization. Due to the limited availability of published information about individuals with psychological disorders, an ensemble SVM algorithm is used as a warning model to improve the accuracy of psychological disorder diagnosis. This algorithm utilizes a differential measurement method to adjust the selection of SVM parameters. The training sample size remains constant, with several iterations of training performed. By combining the differential measurement method, a specific parameter combination is obtained, maximizing the differentiation between base classifiers. This ultimately addresses issues such as small sample size and insufficient sample information in the dataset. Experimental results demonstrate that the model used in this study exhibits good performance in diagnosing psychological disorders.

Information Security and Risk Control Model Based on Plan-Do-Check-Action for Digital Libraries

To continuously and effectively control the potential risk factors of digital libraries, further improve the quality of digital library usage, ensure the smooth operation and information security of digital libraries, a security and risk factor control model for digital libraries based on the Plan-Do-Check-Action is constructed. A digital library security and risk identification model based on improved support vector machines is constructed. Based on the obtained risk identification results, a Plan-Do-Check-Action management model is implemented for information security in digital libraries. The R2L dataset in the network attack dataset is used for training and testing model performance. Then the proposed method is applied to two different digital libraries. The average accuracy of the risk identification model proposed in the research is 96.46%. The risk error of each level is maintained within 0.4%, which is within an acceptable range. The proposed method has a high recognition effect. It can be used to identify common security and risk issues encountered in digital libraries, and improve the information security performance.

Retracted: Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Retracted: Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm

Retracted: Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Retracted: Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Improved SVM Algorithm Financial Management Model for Data Mining

Financial crisis early warning is an important link in financial management. With its relatively simple structure and excellent classification performance, support vector machine is often used in intelligent warning of financial crises. A financial warning model based on improved support vector machine is proposed to address the shortcomings of current financial warning methods that cannot handle large-scale data well, and the accuracy and efficiency are not ideal. In response to the excessive indicators in the financial early warning indicator system, the principal component analysis method is studied for dimensionality reduction, reducing the computational complexity of the model, and improving the training and operational efficiency of the model. In response to the limited practicality of support vector machine, which can only search for the optimal solution under constraint conditions, the use of smooth support vector machine to replace support vector machine for predictive classification is studied. Finally, a financial crisis early warning model is constructed based on smooth support vector machine using principal component analysis for dimensionality reduction of input data. The performance of the model is tested using an internet listed company as an example. The findings demonstrate that Model 1 outperforms the other three models in terms of accuracy rate (95%), MAE, MSE and [Formula: see text] values (0.162, 0.174 and 0.169, respectively). Therefore, the financial crisis early warning model constructed by the research can properly forecast the company’s financial situation, thereby helping the enterprise to develop better.

Research on Path Optimization of Vehicle-Drone Joint Distribution considering Customer Priority

To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. Finally, combined with the numerical example, the optimization scheme of the vehicle-drone joint distribution path considering priority is analyzed. Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. Compared with the results of the scheme that does not consider priority, the results show that priority can significantly improve customer satisfaction. The efficiency of the vehicle-drone joint distribution was verified by comparing the three scenarios.

Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification.

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology.

Hybrid Bio-Inspired Optimization-based Cloud Resource Demand Prediction using Improved Support Vector Machine

Hybrid Bio-Inspired Optimization-based Cloud Resource Demand Prediction using Improved Support Vector Machine

Enterprise financial anomaly data detection method based on improved support vector machine

Enterprise financial anomaly data detection method based on improved support vector machine

An optimizing method of Japanese character recognition based on improved support vector machine

An optimizing method of Japanese character recognition based on improved support vector machine

Time-segment Photovoltaic Forecasting and Uncertainty Analysis Based on Multi-objective Slime Mould Algorithm to Improve Support Vector Machine

Time-segment Photovoltaic Forecasting and Uncertainty Analysis Based on Multi-objective Slime Mould Algorithm to Improve Support Vector Machine

Improving Support vector machine for Imbalanced big data classification

A significant proportion of one type of pattern and a relatively small quantity of another type of pattern can be found in many unbalanced real data sets. In addition, finding significant observations and excluding influential observations is effectively accomplished through diagnostic analysis. Support vector machines (SVM), a common classification technique, perform poorly on imbalanced datasets and when influential observations exist. In this research, the pigeon optimization algorithm as a metaheuristic algorithm is employed to address the influence observation issues in SVM. Experiments are done on three real sets of data. Our approach provides higher classification accuracy compared to other widely used algorithms. This approach could be used for further biological, chemical, and medical datasets.

Modeling and detection of low-altitude flight conflict network based on SVM

With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention. Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping. And use intelligent optimization algorithms to pre train key parameters, achieving efficient conflict detection suitable for low altitude flight.

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

The occurrence of debris flows are a significant threat to human lives and property. Estimating the debris flow scale is a crucial parameter for assessing disaster losses in such events. Currently, the commonly used method for estimating debris flow runoff relies on fitting techniques, which often yield low prediction accuracy and limited data representation capabilities. Addressing these challenges, this study proposes an improved grey wolf algorithm optimized support vector machine prediction model. The model’s effectiveness is validated using data from 72 debris flow events in Beichuan County. The results demonstrate a prediction accuracy of 95.9% using this approach, indicating its strong predictive capabilities for debris flow scale. Additionally, it is observed that the basin area, the basin relative, and the main channel length are the key factors influencing debris flow scale in Beichuan County.

Application of improved support vector machine model in fault diagnosis and prediction of power transformers

AbstractPower transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K‐means algorithm into the model and constructs a high‐precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

Application of multi-source data fusion of modern dress design with the artistic elements of Chinese embroidery

Abstract This paper first investigates the method of extracting features from Chinese embroidery art images, including preprocessing, filtering, and enhancement steps. Then, the multi-classification recognition technique based on an improved support vector machine is used to classify and recognize different modern dress designs. Non-iterative and conjugate orthogonal algorithms are proposed for a feature fusion method based on partial least squares analysis. In the performance analysis part, the performance of feature extraction classification, data fusion energy, and fused data noise are evaluated. Lastly, the impact of the dress style generation map incorporating fused Chinese embroidery elements is analyzed. The study shows that the method in this paper can save 33.4 J of energy consumption after running for 250 s compared with the popular learning-based method. At least a 10% improvement in the number of dress style graphs that interest social event participants, etiquette service practitioners, and performers was achieved after multiple training iterations.

Intelligent Selection Method for Gravity-suitable Area Based on Improved Support Vector Machine by the Genetic Algorithm

The gravity-aided inertial navigation system (GAINS) can achieve precise positioning of underwater vehicles in a gravity-suitable area. However, there are generally shortcomings in the existing intelligent suitable area selection methods in terms of selecting gravity feature parameters and learning parameters. In this paper, an intelligent suitable area selection method is proposed based on an improved support vector machine by the genetic algorithm (GA-SVM) to address the aforementioned problems. Firstly, the genetic algorithm (GA) is utilized to independently pick out the optimal feature subset of 15 existing gravity feature parameters and obtain the optimal support vector machine (SVM) learning parameters while eliminating irrelevant redundant features, thus improving the classifier’s performance and generalization ability. Then, the SVM classifier is trained according to the optimal information output by GA, and the accuracy of the test set is 95.5%. Finally, the classifier is utilized to distinguish suitable and unsuitable areas in the application region to evaluate the proposed method’s performance. The TERCOM experiment in the suitable area resulted in an average positioning error of less than 170 m.